ARS an adaptive retransmission scheme for contention based MAC protocols in underwater acoustic sensor network

Due to the limited capacity and high propagation delay of underwater communication channels, contention-based media access control MAC protocols sufer from a low packet delivery ratio PD

Research Article

ARS: An Adaptive Retransmission Scheme for Contention-Based MAC Protocols in Underwater Acoustic Sensor Networks

Thi-Tham Nguyen and Seokhoon Yoon

Department of Electrical and Computer Engineering, University of Ulsan, Ulsan 680-749, Republic of Korea

Correspondence should be addressed to Seokhoon Yoon; seokhoonyoon@ulsan.ac.kr

Received 11 August 2014; Accepted 13 January 2015

Academic Editor: Nianbo Liu

Copyright © 2015 T.-T Nguyen and S Yoon his is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Due to the limited capacity and high propagation delay of underwater communication channels, contention-based media access control (MAC) protocols sufer from a low packet delivery ratio (PDR) and a high end-to-end (E2E) delay in underwater acoustic sensor networks due to the reliance on packet retransmission for reliable data delivery In order to address the problem of low performance, we propose a novel adaptive retransmission scheme, named ARS, which dynamically selects an optimal value of the maximum number of retransmissions, such that the successful delivery probability of a packet is maximized for a given network load ARS can be used for various contention-based protocols and hybrid MAC protocols that have contention periods In this paper, ARS is applied to well-known contention-based protocols, Aloha and CSMA Simulation results show that ARS can achieve signiicant performance improvement in terms of PDR and E2E delay over original MAC protocols

1 Introduction

Underwater acoustic sensor networks (UASNs) have received

growing interest due to their potential application to

oceano-graphic data collection, environment monitoring, undersea

exploration, disaster prevention, assisted navigation, and

Unfortunately, establishing an efective UASN brings

about new challenges due to unique characteristics of the

underwater acoustic communication channel First, the

underwater acoustic communication channel has a high

propagation delay due to the low speed of acoustic signals,

which is approximately 1500 m/s, ive orders of magnitude

slower than radio waves Second, the available bandwidth for

an acoustic channel is limited, which leads to a low data rate,

high bit error rate is another challenge on an underwater

Media access control (MAC) protocols for UASNs have

been extensively studied to mitigate the limitations of

under-water communication channels Among a lot of MAC

pro-tocols that have been studied for UASNs, contention-based

attention due to their low complexity and high applicability

contention-based MAC protocol can achieve acceptable throughput and low latency with a low network load without

Contention-based MAC protocols for a UASN can be further classiied into handshake-based and random access-based protocols here have been a lot of studies on

to address the long propagation delay in UASNs However, the exchange of control packets causes a long packet delay, and control packets also have a long preamble, which leads

protocols are not appropriate for applications that require a low delay

here have also been a considerable number of studies

comes from their reliance on packet retransmission More speciically, they depend on retransmission for reliable data delivery, which is suitable for terrestrial wireless networks However, in a UASN, packet retransmission can quickly

Volume 2015, Article ID 826263, 15 pages

http://dx.doi.org/10.1155/2015/826263

saturate the network due to the limited channel capacity,

which results in a high level of packet collisions and the

consequent low PDR

Moreover, due to the high propagation delay of the

under-water acoustic communication channel, the MAC protocol

requires a long slot duration, which leads to a long back-of

interval and end-to-end delay In other words, the unique

characteristics of the underwater acoustic communication

channel make existing packet retransmission strategies

pro-posed for terrestrial wireless networks unsuitable for UASNs

herefore, in a communication environment with a

lim-ited channel capacity, the decision on retransmission should

be carefully made so as not to impose a high network load that

can inadvertently result in very low performance in terms of

PDR and E2E delay

In order to address this issue, we propose an adaptive

retransmission based MAC scheme, named ARS, which

selects an optimal value of maximum number of

retransmis-sions that is adapted to the network load such that successful

packet delivery probability (PDP) is maximized

ARS periodically calculates a PDP value using the current

maximum number of retransmissions (or maximum

retrans-missions) and then compares it with the estimated PDP

values that are calculated by increasing and decreasing the

maximum number of retransmissions hen, ARS chooses

a new value for the maximum retransmissions with which

a higher PDP value can be achieved Simulation results

show that ARS can achieve higher performance in terms

of PDR and E2E delay compared to the existing schemes

In particular, when the network load changes, ARS also

shows higher performance than the existing algorithms Note

that sensors in a sensor network may increase the sensor

data transmission rate when speciic events occur or some

conditions are met

It is also worthwhile to note that ARS can be applied

not only to pure contention-based MAC protocols (including

Aloha, Aloha-CS, and CSMA) but also to hybrid MAC

protocols that employ contention periods (i.e., by using ARS

the performance of data transmission in contention periods

can be improved)

he rest of this paper is organized as follows Sections

respectively hen, we elaborate on the proposed ARS scheme

and suggests future work

2 Related Work

MAC protocols for a UASN can be divided into

contention-free and contention-based protocols he contention-contention-free

protocols consist of frequency division multiple access

(FDMA), time division multiple access (TDMA), and code

division multiple access (CDMA), in which they assign

diferent frequency bands, time slots, or spreading codes to

diferent users to avoid collisions among transmissions In

the contention-based protocols, on the other hand, the nodes

need to compete to access the shared channel

It is already known that FDMA is not suitable for UASNs due to the limited available bandwidth of underwater acoustic channels TDMA requires a large guard time and

it is known that CDMA-based protocols require a high-complexity design for UASNs In particular, it is necessary

to design access codes with high autocorrelation and low cross-correlation properties to achieve minimum

In contrast, contention-based MAC protocols, most of

recently received signiicant attention for UASNs due to

fur-ther classiied into handshake-based protocols and random access-based protocols

A lot of handshake-based protocols have been studied

the propagation delay tolerant collision avoidance protocol (PCAP) In PCAP, in order to take advantage of a long prop-agation delay, while the sender is waiting for the clear to send (CTS) packet, it is allowed to transmit another data packet

or perform a handshake for the next queued data packet PCAP requires clock synchronization between neighboring nodes Another handshake-based protocol, called distance-aware collision avoidance protocol (DACAP), was proposed

CTS, the sender waits for a speciic time before transmitting the data packet in order to ensure the sender can receive any warning from the intended receiver to avoid the collisions

he length of the waiting period depends on the distance between sender and receiver

Note that those handshake-based protocols can cause a long packet delay due to the exchange of control packets prior to actual data transmission Moreover, those control packets also have a long preamble in a practical underwater communication environment, which results in low network

Another approach to channel contention resolution is to

tone-based protocol called T-Lohi In T-Lohi, prior to data trans-mission, a node transmits a short tone to inform its neighbors about the transmission and receives tone signals from other nodes (which may arrive at diferent time instances due to diferent propagation delays) to detect the number of channel contenders If the node does not receive any tones, it starts data transmission Otherwise, it performs a backof with

a back-of interval calculated using the number of tones received However, T-Lohi nodes need special hardware for

a wake-up tone receiver to detect tones using low energy consumption

here have also been a lot of studies on random

Aloha with collision avoidance (Aloha-CA) and Aloha with advance notiication (Aloha-AN) hese two schemes utilize information obtained from overheard packets plus informa-tion about propagainforma-tion delays between every node pair in the network to calculate other nodes’ busy durations, which are

maintained in the local database table of each node When

a node has a packet to transmit, in Aloha-CA, the node

checks the busy durations of other nodes in its database

table to determine whether its transmission would cause a

collision In the event of a possible collision, the node defers

transmission for a random time In Aloha-AN, a sender also

performs a collision check using its database table If no

collision is foreseen, it transmits a small notiication packet

to inform other nodes about its pending data transmission

Another extension of the Aloha protocol is Aloha-CS

protocol for UASNs because it ofers high throughput and low

latency and does not require time synchronization or a

Aloha-based protocol, called propagation delay tolerant Aloha

(PDT-Aloha), where the authors try to handle the space-time

uncertainty in underwater acoustic channels Nodes transmit

only at the start of globally synchronized slots he spatial

uncertainty is handled by adding a guard time, which is

proportional to the propagation delay

A major disadvantage to these random access-based

MAC protocols is that they need to rely on a

retrans-mission mechanism for reliable data delivery Since packet

retransmissions can increase network traic signiicantly,

the decision on packet retransmission should be carefully

made so as not to degrade network performance In order to

address this issue, the goal of our work is to design a MAC

scheme that can determine an optimal value of the maximum

number of retransmissions based on network load so that

the packet delivery ratio is maximized with a low end-to-end

delay and without requiring time synchronization and special

hardware

Note that some protocols take a hybrid approach that

uses features of both TDMA or CDMA and random-access

a hybrid of scheduling and a random-access protocol for

UASNs hey divided the channel into several superframes,

which contain broadcast, gathering, and event report periods

During the broadcast and gathering periods, each sensor

broadcasts and gathers data in a predetermined time slot,

where it can transmit data while avoiding collisions On the

other hand, during the event report period, sensor nodes use

a random-access protocol to report the sensed events that can

not be transmitted using prescheduled time slots

One beneit of a hybrid protocol is that it can provide

diferentiated services and quality of service (QoS) For

example, the superframe in a hybrid protocol can consist

of a contention-free period (CFP) and contention period

(CP) In the CFP, time slots are assigned to sensor nodes so

that the high-priority data (or data that require a low delay)

can be transmitted without collisions In contrast, for

low-priority data or non-real-time data, sensor nodes contend for

channel access using a random-access protocol (e.g., CSMA

and Aloha) during the CP Note that ARS can be applied

to those hybrid protocols to increase network performance

during CPs

It is worthwhile to note that our work is signiicantly

diferent from the existing studies on retransmission schemes

models, assumptions, and algorithms For example, the study

also assumes that the transmitting node can detect packet

the number of blocked stations is known for optimal retrans-mission hose assumptions are not practical in underwater networks due to a high propagation delay In contrast, our work does not require time synchronization, packet collision detection during transmission, and information on the number of blocked stations

which nodes would transmit a packet in advance and the base station monitors whether or not all expected packets are successfully received hen, it uses a separate control channel to transmit a busy signal to all successful nodes until all collided packets are retransmitted successfully Our protocol does not use a separate control channel and nodes

do not need to wait until all collided packets are retransmitted successfully

of transmitter-only nodes, which have only an RF transmitter without an RF receiver he sending nodes transmit each packet ixed and predetermined times; that is, the number

of total transmissions of each packet is predetermined before

that the network status (e.g., the number of nodes and network loads) does not change during the network life time Since the network status information is known and each node transmits each packet predetermined times, inding a solution that maximizes the packet delivery probability is rather simple and straightforward

In contrast, we assume that the network status varies over time herefore, the algorithm repeatedly compares the PDP (packet delivery probability) value when the value of the maximum number of retransmissions is decremented and incremented his process continues to ind the optimal value

of the maximum number of retransmissions Note that this approach involves another algorithm: approximation of the PDP values with the incremented and decremented values

of the maximum number of retransmission In addition, in

since every node transmits the packet predetermined times and thus the total traic can be controlled However, in this work, the total traic can not be known since the number of packet transmissions are not predetermined

3 System Model

he UASN under consideration has a cluster-based network topology where each underwater sensor node belongs to one cluster governed by a clusterhead It is known that a cluster-based UASN provides suitable network connectivity and scalability in underwater communication environments

Each underwater sensor node transmits sensing data using a direct acoustic channel to its clusterhead, which

performs data aggregation and then forwards the data to the

sink node Clusterheads are equipped with two underwater

communication interfaces, one for intracluster

communi-cations, the other for intercluster communications It is

assumed that communications in one cluster do not interfere

with communications in other clusters because they use

Each sensor node transmits to the clusterhead a data

clusterhead immediately responds with an acknowledgement

(ACK) packet to the source node

In this paper, to facilitate presentation, we focus on an

sensor node can transmit to the clusterhead the same copy of

retransmitted packets, if it has not received an ACK packet

within the ACK timeout interval

Also, the packet delivery probability represents the

suc-cessful delivery probability of a packet when the packet can

ratio (PDR) refers to the ratio of the number of successfully

delivered packets to the number of the packets transmitted,

which is usually collected by simulations and experiments

4 Algorithms

In this section, we describe the detailed algorithm of ARS

of retransmissions) to maximize packet delivery probability

(PDP), which leads to a high PDR and a low end-to-end delay

First, we discuss the assumption that packet arrivals

follow a Poisson process, and we justify that the assumption is

acceptable in a UASN where underwater nodes may perform

exponential back-of and carrier sensing hen, we elaborate

on how to obtain the PDP value with the current maximum

values Finally, we describe the selection of an optimal value

4.1 Preliminary When the packet arrivals follow a Poisson

In this paper, the arrival rate of the background traic

Now, suppose that a data packet arrives at the clusterhead

packet not to collide at the clusterhead, none of the packets

herefore, the probability that a data packet is successfully

given by

4.2 Estimating PDP with the Current Maximum Number

hen, we extend our discussion to obtain the packet delivery

information needed is the arrival rate of background traic

In ARS, each node periodically reports to the clusterhead the load it has generated More speciically, an arbitrary node

and the total number of transmitted packets, including those

and sends it to the clusterhead

packets and the total number of packets transmitted,

as follows:

(3)

he clusterhead then calculates the average number of

the arrival rate of background traic generated by the other

follows:

hen, the probability that a single packet transmission

is successfully delivered to the clusterhead can be

Now, we discuss the calculation of the PDP when a packet

packet, respectively

Since each packet transmission can be regarded as an independent event based on the assumption of a Poisson

process,�(�,�) = ��and�(�,�) = ��for all� herefore, PDP

can be expressed as

�

4.3 Estimating PDP with the Maximum Number of

values with two diferent values of the maximum number of

the average number of retransmissions over diferent values

obtain new PDP values

� − 1

represents actual retransmissions when the maximum

the probability that the actual number of retransmissions is

(7)

node will not transmit the packet any more

Now, we take into account the fact that, for a given integer

�

�

�

�

(8)

Now, expected retransmissions with maximum

simulation results show that this approximation works well

be increased, decreased, or stay the same In fact, all the information needed is whether the PDP value is increasing

4.4 Selecting an Optimal Value of the Maximum Number

of Retransmissions Using Estimated PDP Values he main

Intuitively, when the network load is low, the clusterhead

When the network load is too heavy, on the other hand, the achievable PDP value is low due to network congestion and

a high level of packet collisions In that case, the clusterhead

PDP value

clusterhead uses a threshold value for a gain in the PDP value More speciically, the decision to change the current

which publishes this value to the network Upon receiving the

the adaptive selection process

5 Performance Study

5.1 Simulation Setup In order to verify that ARS can improve network performance in terms of PDR and E2E delay, we compare the performance of ARS-applied protocols with that

of the existing contention-based MAC protocols

In this paper, we select Aloha and CSMA for performance comparison, since a lot of contention-based MAC protocols are based on Aloha and CSMA he design, simulate, emu-late, and realize test-beds (DESERT) underwater simulation

protocols in a realistic underwater communication environ-ment

he cluster considered for the simulation consists of 50 underwater sensor nodes randomly deployed over an area of

�: number of sensor nodes

��: time interval

�: maximum number of retransmissions for irst interval ��

�tr: transmission delay

�ori: total number of original packets in the network ater duration of��

�tot: total number of packets transmitted in the network ater duration of��

��: average number of retransmissions for each packet

�: system parameter

�: threshold value (� > 0)

Outputs:

�opt: he optimal value of number of retransmissions for next interval��

(1) while (true) do

//Estimate the PDP at current maximum number of retransmissions�(�):

(2) ��= �tot

�ori

; (3) ��= ��× �ori

�� × (� − 1)� ; (4) ��= �−2��� tr

; (5) ��= 1 − �−2��� tr

; (6) �(�) = 1 − (1 − �−2��� tr)�;

//Estimate the PDP at incremented and decremented value of current maximum retransmissions�(� + 1), and �(� − 1): (7) �(��) = 1 − �

�

�

1 − ��;� (��+1) =1 − �

�+1

�

1 − �� ;� (��−1) = 1 − �

�−1

�

1 − �� ;

(8) �inc= � (��+1) − � (��); �dec= � (��) − � (��−1);

(9) ��+1= ��+ � × �inc;��−1= ��− � × �dec;

�� × (� − 1)� ;

�� × (� − 1)� ; (12) �(� + 1) = 1 − (1 − �−2��(�+1)� tr)�+1;

(13) �(� − 1) = 1 − (1 − �−2� �(�−1) � tr)�−1;

//Select the optimal value of maximum number of retransmissions�opt:

(14) if (�(� + 1) > �(� − 1)) then

(15) if �(� + 1) − �(�) ≥ � then

(17) else

(19) end if

(20) else

(21) if �(� − 1) − �(�) ≥ � then

(23) else

(25) end if

(26) end if

(27) Output�opt;

(28) Re-read�ori,�tot;

(29) end while

Algorithm 1: he adaptive selection algorithm

1 2 3 4 5 6

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Network load (kbps)

CSMA-ARS CSMA CSMA

CSMA CSMA

w/ x = 1

w/ x = 3

w/ x = 5 w/ x = 7 (a)

Network load (kbps) CSMA-ARS

CSMA CSMA

CSMA CSMA

0 5 10 15 20 25 30 35 40 45

w/ x = 1

w/ x = 3

w/ x = 5 w/ x = 7 (b)

Figure 1: CSMA: efects of network load on (a) PDR and (b) average end-to-end delay

with a half-duplex acoustic transceiver that has a data rate

of 14 kbps at a distance of 1100 m It is assumed that each

underwater sensor node periodically generates a data packet

of 160 bytes and sends the data packet to the clusterhead

he speed of underwater acoustic signals is assumed to be

1500 m/s

5.2 Simulation Results We analyze network performance

in terms of PDR and average end-to-end delay First, we

discuss the efects of network load on network performance

he dynamic network load during the simulation is also

considered to show that ARS can adaptively ind an optimal

value of maximum retransmissions based on varying network

traic hen, we analyze network performance over diferent

numbers of data lows Finally, we present the efects of the

weighted factor on performance

5.2.1 Efects of Network Load In order to examine the efects

of network load, the data transmission rate of the nodes varies

over the simulations he transmission rate of each node is

varied from 20 bps to 120 bps, which results in total network

load from 1 kbps to 6 kbps Diferent values for the maximum

number of retransmissions under CSMA and Aloha protocols

are tested, (i.e., 1, 3, 5, and 7 are used for the maximum

retransmissions)

Figure 1compares the efects of network load on CSMA

with ARS (referred to as simply CSMA-ARS hereater) and

when network load is low (e.g., 1 kbps), the achieved PDR

by CSMA-ARS is similar to CSMA with a large value of the maximum retransmissions (e.g., 5 and 7) On the other hand, when network load is high (e.g., from 4 kbps to 6 kbps), CSMA-ARS can achieve a similar performance to CSMA with the maximum number of retransmissions of 1 CSMA-ARS

network load is between 4 kbps and 6 kbps his is because

reach an optimal point

Figure 1(a) also indicates that if network load varies over time, CSMA-ARS can achieve over 20% higher PDR

optimal value of the maximum retransmissions over diferent network loads

he end-to-end delays over diferent network loads are

than 20 seconds in some cases due to a large number of packet

by sacriicing PDR at a low network load Note that the E2E delay reaches a peak and decreases, since most packets are dropped under a very high network load, and those dropped packets are not considered when calculating the delay ARS keeps adjusting the value of maximum number of

ARS takes can actually achieve a higher PDR value with a low E2E delay compared to the CSMA protocol

Figure 2compares the performance of Aloha with ARS and Aloha as network load varies Similar to the simulation

1 2 3 4 5 6

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Network load (kbps)

Aloha-ARS Aloha Aloha

Aloha Aloha

w/ x = 1

w/ x = 3

w/ x = 5 w/ x = 7 (a)

0 5 10 15 20 25 30 35 40 45

Network load (kbps) Aloha-ARS

Aloha Aloha

Aloha Aloha

w/ x = 1

w/ x = 3

w/ x = 5 w/ x = 7 (b)

Figure 2: Aloha: efects of network load on (a) PDR and (b) average end-to-end delay

obtain the PDR value that is close to the maximum PDR

speciically, when network load is low (1 kbps or 2 kbps)

Aloha-ARS can achieve similar PDR and E2E delay values to

other hand, the achieved PDR and E2E delay values of

Aloha-ARS are similar to those of Aloha when Aloha uses a low value

Another interesting point is that, as shown in Figures

CSMA as the network load grows his is because more packet

collisions can occur in Aloha under a high network load

due to the lack of carrier sensing However, Aloha-ARS and

CSMA-ARS show a similar PDR over diferent network loads,

which indicates that ARS can lower the number of packet

Figure 3compares the PDR and delay between

load is low, CSMA-ARS and Aloha-ARS achieve a similar

PDR, which is close to one However, when network load is

relatively high (around 3 kbps), CSMA-ARS shows a higher

PDR since carrier sensing can reduce packet collisions

In case network load is very high, both protocols achieve

relatively low PDR values due to the limited channel capacity

a lower delay because it does not have latency for carrier

sensing

PDR values during each round when the network load is

4 kbps, and it also shows how ARS interacts with those values

he instantaneous PDR, which is collected from simulations,

is deined as the ratio of the number of received packets to

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Network load (kbps)

0 1 2 3 4

5

PDR (CSMA-ARS) PDR (Aloha-ARS)

Delay (CSMA-ARS) Delay (Aloha-ARS)

Figure 3: PDR and delay between CSMA-ARS and Aloha-ARS with diferent network load

the number of packets transmitted to the channel within one

instantaneous PDR

0 5 10 15 20 25 30 35

1

2

3

4

5

Round of x determination

(a)

Round of x determination 0

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Instantaneous PDR P(x)

P(x + 1) P(x − 1) (b)

Figure 4: CSMA: (a) adaptive maximum number of retransmissions and (b) comparison of instantaneous PDR and�(�)

1

2

3

4

5

Round of x determination

(a)

Round of x determination 0

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

P(x)

P(x + 1) P(x − 1) Instantaneous PDR

(b) Figure 5: Aloha: (a) adaptive maximum number of retransmissions and (b) comparison of instantaneous PDR and�(�)

at round 7, the clusterhead decides to increase the value of

� to 2, since �(� + 1) is higher than �(�) and keeps this

PDP) can closely approximate the instantaneous PDR as the

network load becomes stable ater round 7 (i.e., the network

load is relatively unstable until round 7 due to the rapid

Figure 5indicates the detailed operation of Aloha-ARS

0 0.96 1.4 1.7 1.9 2.45

×105 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Simulation time (s)

CSMA-ARS

CSMA

CSMA

CSMA CSMA

w/ x = 1

w/ x = 3

w/ x = 5 w/ x = 7 (a)

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

×105 Simulation time (s)

Aloha-ARS Aloha Aloha

Aloha Aloha

w/ x = 1

w/ x = 3

w/ x = 5 w/ x = 7 (b)

Figure 6: Instantaneous PDR when varying the network load over time (a) under CSMA and (b) under Aloha

the simulation since the clusterhead determines the optimal

(i.e., PDP) can closely approximate the instantaneous PDR

5.2.2 Varying Network Load Over Time In some sensor

network applications, sensors may increase the sensor data

transmission rate when speciic events occur or some

con-ditions are satisied (e.g., a speciic level of vibration or

temperature)

In order to see how ARS can adapt to a change in network

load over time, every node varies its packet generation rate

over a simulation time of 245,000 seconds More speciically,

from the beginning, each node has a rate of 20 bps for 96,000

seconds, which results in a network load of 1 kbps hen,

the generation rate of each node increases to 40 bps in the

next round time period of 44,000 seconds (the network load

becomes 2 kbps) For the next 30,000 seconds, the rate of

each node becomes 80 bps, and then it becomes 120 bps (the

network load is 6 kbps) for the next 20,000 seconds hen,

each node decreases its traic rate to 20 bps for the rest of the

simulation

(or “inst PDR” for short) of CSMA-ARS and Aloha-ARS

with those of CSMA and Aloha with diferent values for the

maximum number of retransmissions

FromFigure 6(a), we can see that CSMA-ARS adapts well

to the change in network load and achieves the highest or

near the highest inst PDR over the entire simulation time

maximum retransmissions to obtain a high PDR value For

example, in the interval from 0 to 96,000 seconds,

avoid excessive collisions For instance, CSMA-ARS obtains

an inst PDR (around 0.5 and 0.38) similar to CSMA with

� = 1 from 140,000 to 190,000 seconds, when network load is very high

In contrast, the original CSMA protocol cannot adapt

to the network load changes and shows poor performance,

5 shows a PDR value of around 0.99 in the time period between 0 and 96,000 seconds However, it obtains a PDR value lower than 0.2 between 170,000 and 190,000 seconds, whereas CSMA-ARS achieves a PDR value of around 0.38 in

of less than 0.85, on average, when network load is low (from

0 to 96,000 seconds) whereas CSMA-ARS can achieve a PDR

of around 0.99

he results are also similar when Aloha-ARS is

Figure 6(b) Aloha-ARS can adaptively determine an optimal

simula-tion time, so it can also achieve the highest or near the highest inst PDR value

In fact, ARS shows a higher advantage in this case since Aloha is more sensitive to the network load For example,

when the network load is high (from 170,000 and 190,000 seconds), whereas it achieves a PDR value of around 0.99 when the network load is low In contrast, Aloha-ARS shows a consistently high PDR value compared to the original Aloha

It can also be seen that Aloha-ARS has similar PDR values to CSMA-ARS in most time periods

Figure 7compares the average PDR and end-to-end delay

of CSMA-ARS and Aloha-ARS with those of CSMA and